Function Reference

The X-Pack machine learning features include analysis functions that provide a wide variety of
flexible ways to analyze data for anomalies.

When you create jobs, you specify one or more detectors, which define the type of
analysis that needs to be done. If you are creating your job by using machine learning APIs,
you specify the functions in
Detector Configuration Objects.
If you are creating your job in Kibana, you specify the functions differently
depending on whether you are creating single metric, multi-metric, or advanced
jobs. For a demonstration of creating jobs in Kibana, see Getting Started.

Most functions detect anomalies in both low and high values. In statistical
terminology, they apply a two-sided test. Some functions offer low and high
variations (for example, count, low_count, and high_count). These variations
apply one-sided tests, detecting anomalies only when the values are low or
high, depending one which alternative is used.

You can specify a summary_count_field_name with any function except metric.
When you use summary_count_field_name, the machine learning features expect the input
data to be pre-aggregated. The value of the summary_count_field_name field
must contain the count of raw events that were summarized. In Kibana, use the
summary_count_field_name in advanced jobs. Analyzing aggregated input data
provides a significant boost in performance. For more information, see
Aggregating Data For Faster Performance.

If your data is sparse, there may be gaps in the data which means you might have
empty buckets. You might want to treat these as anomalies or you might want these
gaps to be ignored. Your decision depends on your use case and what is important
to you. It also depends on which functions you use. The sum and count
functions are strongly affected by empty buckets. For this reason, there are
non_null_sum and non_zero_count functions, which are tolerant to sparse data.
These functions effectively ignore empty buckets.